This is an independent re-analysis of the data published by Islam et al.
In this analysis, we have processed and aligned all datasets and from this stage onward focused only on datasets with a certain minimum coverage -- medium = at least 250,000 aligned reads or high = at least 500,000 aligned reads.

Description from GEO:

Summary Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constitutent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-Seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data was projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves -- all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the complete transcriptomes of 92 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology and disease.

Other data generated in this experiment:

Typical analysis workflows my generate dozens or even hundreds of outputs.
To condense the amount of information into more easily digestible portions,
GeneProf will, by default, only display the experiments input data (here: input data)
and a selection of the most important outputs (here: main results).
You can drill down into the details of the analysis via the workflow designer or
you can display other intermediate outputs here.

Analysis Workflow

This is a schematic representation of the analysis workflow used in this experiment. For more details (parameters, etc.) use the fully-featured workflow designer.

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